import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d

model_urls = {
	'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
	'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
	'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
	'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
	'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
BaseNet_version = 'DLab3+34'

def conv3x3(in_planes, out_planes, stride=1):
	"""3x3 convolution with padding"""
	return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
					 padding=1, bias=False)


class BasicBlock(nn.Module):
	expansion = 1
	
	def __init__(self, inplanes, planes, stride=1, downsample=None):
		super(BasicBlock, self).__init__()
		self.conv1 = conv3x3(inplanes, planes, stride)
		self.bn1 = nn.BatchNorm2d(planes)
		self.relu = nn.ReLU(inplace=True)
		self.conv2 = conv3x3(planes, planes)
		self.bn2 = nn.BatchNorm2d(planes)
		self.downsample = downsample
		self.stride = stride
	
	def forward(self, x):
		residual = x
		
		out = self.conv1(x)
		out = self.bn1(out)
		out = self.relu(out)
		
		out = self.conv2(out)
		out = self.bn2(out)
		
		if self.downsample is not None:
			residual = self.downsample(x)
		
		out += residual
		out = self.relu(out)
		
		return out


class ResNet_0(nn.Module):
	def __init__(self, block, layers, num_classes=1000, deep_base=False, stem_width=32):
		self.inplanes = stem_width * 2 if deep_base else 64
		
		super(ResNet_0, self).__init__()
		if deep_base:
			self.conv1 = nn.Sequential(
				nn.Conv2d(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False),
				nn.BatchNorm2d(stem_width),
				nn.ReLU(inplace=True),
				nn.Conv2d(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False),
				nn.BatchNorm2d(stem_width),
				nn.ReLU(inplace=True),
				nn.Conv2d(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False),
			)
		else:
			self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
								   bias=False)
		
		self.bn1 = nn.BatchNorm2d(self.inplanes)
		self.relu = nn.ReLU(inplace=True)
		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
		self.layer1 = self._make_layer(block, 64, layers[0])
		self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
		self.layer3 = self._make_layer(block, 256, layers[2], stride=1, atrous=2)
		self.layer4 = self._make_layer(block, 512, layers[3], stride=1, atrous=2)
		self.avgpool = nn.AvgPool2d(7, stride=1)
		self.fc = nn.Linear(512 * block.expansion, num_classes)
		
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
				m.weight.data.normal_(0, math.sqrt(2. / n))
			elif isinstance(m, nn.BatchNorm2d):
				m.weight.data.fill_(1)
				m.bias.data.zero_()
	
	def _make_layer(self, block, planes, blocks, stride=1, atrous=1):
		downsample = None
		if stride != 1 or self.inplanes != planes * block.expansion:
			downsample = nn.Sequential(
				nn.Conv2d(self.inplanes, planes * block.expansion,
						  kernel_size=1, stride=stride, dilation=atrous, bias=False),
				nn.BatchNorm2d(planes * block.expansion),
			)
		
		layers = []
		layers.append(block(self.inplanes, planes, stride, downsample))
		self.inplanes = planes * block.expansion
		for i in range(1, blocks):
			layers.append(block(self.inplanes, planes))
		
		return nn.Sequential(*layers)
	
	def forward(self, x):
		x = self.conv1(x)
		x = self.bn1(x)
		x = self.relu(x)
		x = self.maxpool(x)
		
		x = self.layer1(x)
		low_level_feat = x
		x = self.layer2(x)
		x = self.layer3(x)
		t = self.layer4(x)
		return t, low_level_feat


class Bottleneck(nn.Module):
	expansion = 4

	def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
		super(Bottleneck, self).__init__()
		self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
		self.bn1 = BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
							   dilation=dilation, padding=dilation, bias=False)
		self.bn2 = BatchNorm2d(planes)
		self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
		self.bn3 = BatchNorm2d(planes * 4)
		self.relu = nn.ReLU(inplace=True)
		self.downsample = downsample
		self.stride = stride
		self.dilation = dilation

	def forward(self, x):
		residual = x

		out = self.conv1(x)
		out = self.bn1(out)
		out = self.relu(out)

		out = self.conv2(out)
		out = self.bn2(out)
		out = self.relu(out)

		out = self.conv3(out)
		out = self.bn3(out)

		if self.downsample is not None:
			residual = self.downsample(x)

		out += residual
		out = self.relu(out)

		return out


class ResNet(nn.Module):
	def __init__(self, nInputChannels, block, layers, os=16, pretrained=False):
		self.inplanes = 64
		super(ResNet, self).__init__()
		if os == 16:
			strides = [1, 2, 2, 1]
			dilations = [1, 1, 1, 2]
			blocks = [1, 2, 4]
		elif os == 8:
			strides = [1, 2, 1, 1]
			dilations = [1, 1, 2, 2]
			blocks = [1, 2, 1]
		else:
			raise NotImplementedError

		# Modules
		self.conv1 = nn.Conv2d(nInputChannels, 64, kernel_size=7, stride=2, padding=3,
								bias=False)
		self.bn1 = BatchNorm2d(64)
		self.relu = nn.ReLU(inplace=True)
		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

		self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0])
		self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
		self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
		self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3])

		self._init_weight()

	def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
		downsample = None
		if stride != 1 or self.inplanes != planes * block.expansion:
			downsample = nn.Sequential(
				nn.Conv2d(self.inplanes, planes * block.expansion,
						  kernel_size=1, stride=stride, bias=False),
				BatchNorm2d(planes * block.expansion),
			)

		layers = []
		layers.append(block(self.inplanes, planes, stride, dilation, downsample))
		self.inplanes = planes * block.expansion
		for i in range(1, blocks):
			layers.append(block(self.inplanes, planes))

		return nn.Sequential(*layers)

	def _make_MG_unit(self, block, planes, blocks=[1, 2, 4], stride=1, dilation=1):
		downsample = None
		if stride != 1 or self.inplanes != planes * block.expansion:
			downsample = nn.Sequential(
				nn.Conv2d(self.inplanes, planes * block.expansion,
						  kernel_size=1, stride=stride, bias=False),
				BatchNorm2d(planes * block.expansion),
			)

		layers = []
		layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation, downsample=downsample))
		self.inplanes = planes * block.expansion
		for i in range(1, len(blocks)):
			layers.append(block(self.inplanes, planes, stride=1, dilation=blocks[i]*dilation))

		return nn.Sequential(*layers)

	def forward(self, input):
		x = self.conv1(input)
		x = self.bn1(x)
		x = self.relu(x)
		x = self.maxpool(x)

		x = self.layer1(x)
		low_level_feat = x
		x = self.layer2(x)
		x = self.layer3(x)
		x = self.layer4(x)
		
		return x, low_level_feat

	def _init_weight(self):
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
				m.weight.data.normal_(0, math.sqrt(2. / n))
			elif isinstance(m, BatchNorm2d):
				m.weight.data.fill_(1)
				m.bias.data.zero_()


def ResNet34(pretrained=True):
	"""
	Constructs a ResNet-34 model.
	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	"""
	model = ResNet_0(BasicBlock, [3, 4, 6, 3])
	model_dict = model.state_dict()
	
	model_path = "./"
	if pretrained:
		pretrained_dict = model_zoo.load_url(model_urls['resnet34'], model_dir=model_path)  # Modify 'model_dir' according to your own path
		print('Petrain resnet34 Model Have been loaded!')
		# pretrained_dict =  {k: v for k, v in pretrained_dict.items() if k in model_dict}
		# model_dict.update(pretrained_dict)
		model.load_state_dict(pretrained_dict)
	return model


def ResNet50(nInputChannels=3, os=16, pretrained=True):
	model = ResNet(nInputChannels, Bottleneck, [3, 4, 6, 3], os)
	model_dict = model.state_dict()
	
	model_path = "./"
	if pretrained:
		pretrained_dict = model_zoo.load_url(model_urls['resnet50'], model_dir=model_path)  # Modify 'model_dir' according to your own path
		print('Petrain resnet50 Model Have been loaded!')
		pretrained_dict =  {k: v for k, v in pretrained_dict.items() if k in model_dict}
		model_dict.update(pretrained_dict)
		model.load_state_dict(pretrained_dict)
	return model


def ResNet101(nInputChannels=3, os=16, pretrained=True):
	model = ResNet(nInputChannels, Bottleneck, [3, 4, 23, 3], os)
	model_dict = model.state_dict()
	
	model_path = "./"
	if pretrained:
		pretrained_dict = model_zoo.load_url(model_urls['resnet101'], model_dir=model_path)  # Modify 'model_dir' according to your own path
		print('Petrain resnet101 Model Have been loaded!')
		pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
		model_dict.update(pretrained_dict)
		model.load_state_dict(pretrained_dict)
	return model


class ASPP_module(nn.Module):
	def __init__(self, inplanes, planes, dilation):
		super(ASPP_module, self).__init__()
		if dilation == 1:
			kernel_size = 1
			padding = 0
		else:
			kernel_size = 3
			padding = dilation
		self.atrous_convolution = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,
											stride=1, padding=padding, dilation=dilation, bias=False)
		self.bn = BatchNorm2d(planes)
		self.relu = nn.ReLU()

		self._init_weight()

	def forward(self, x):
		x = self.atrous_convolution(x)
		x = self.bn(x)

		return self.relu(x)

	def _init_weight(self):
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
				m.weight.data.normal_(0, math.sqrt(2. / n))
			elif isinstance(m, BatchNorm2d):
				m.weight.data.fill_(1)
				m.bias.data.zero_()


class DeepLabv3_plus(nn.Module):
	def __init__(self, nInputChannels=3, n_classes=1, os=16, pretrained=True, freeze_bn=False, _print=True):
		if _print:
			print("Constructing DeepLabv3+ model...")
			print("Backbone: Resnet-101")
			print("Number of classes: {}".format(n_classes))
			print("Output stride: {}".format(os))
			print("Number of Input Channels: {}".format(nInputChannels))
		super(DeepLabv3_plus, self).__init__()
		
		self.backbone = '34'
		# Atrous Conv
		if self.backbone == '34':
			self.resnet_features = ResNet34(pretrained=pretrained)
			c_aspp = 512
			c_2 = 64
		elif self.backbone == '50':
			self.resnet_features = ResNet50(nInputChannels, os, pretrained=pretrained)
			c_aspp = 2048
			c_2 = 256
		elif self.backbone == '101':
			self.resnet_features = ResNet101(nInputChannels, os, pretrained=pretrained)
			c_aspp = 2048
			c_2 = 256
		
		# ASPP
		if os == 16:
			dilations = [1, 6, 12, 18]
		elif os == 8:
			dilations = [1, 12, 24, 36]
		else:
			raise NotImplementedError

		self.aspp1 = ASPP_module(c_aspp, 256, dilation=dilations[0])
		self.aspp2 = ASPP_module(c_aspp, 256, dilation=dilations[1])
		self.aspp3 = ASPP_module(c_aspp, 256, dilation=dilations[2])
		self.aspp4 = ASPP_module(c_aspp, 256, dilation=dilations[3])
		self.relu = nn.ReLU()
		self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
											 nn.Conv2d(c_aspp, 256, 1, stride=1, bias=False),
											 BatchNorm2d(256),
											 nn.ReLU())
		self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
		self.bn1 = BatchNorm2d(256)
		
		# adopt [1x1, 48] for channel reduction.
		self.conv2 = nn.Conv2d(c_2, 48, 1, bias=False)
		self.bn2 = BatchNorm2d(48)
		
		self.sigmoid = nn.Sigmoid()
		self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
									   BatchNorm2d(256),
									   nn.ReLU(),
									   nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
									   BatchNorm2d(256),
									   nn.ReLU(),
									   nn.Conv2d(256, n_classes, kernel_size=1, stride=1))

	def forward(self, input):
		x, low_level_features = self.resnet_features(input)
		x1 = self.aspp1(x)
		x2 = self.aspp2(x)
		x3 = self.aspp3(x)
		x4 = self.aspp4(x)
		x5 = self.global_avg_pool(x)
		x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)

		x = torch.cat((x1, x2, x3, x4, x5), dim=1)
		x = self.conv1(x)
		x = self.bn1(x)
		x = self.relu(x)
		
		x = F.interpolate(x, size=(int(math.ceil(input.size()[-2]/4)),
								int(math.ceil(input.size()[-1]/4))), mode='bilinear', align_corners=True)

		low_level_features = self.conv2(low_level_features)
		low_level_features = self.bn2(low_level_features)
		low_level_features = self.relu(low_level_features)


		x = torch.cat((x, low_level_features), dim=1)
		x = self.last_conv(x)
		x = F.interpolate(x, size=input.size()[2:], mode='bilinear', align_corners=True)
		x = self.sigmoid(x)
		return x

	def _init_weight(self):
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
				m.weight.data.normal_(0, math.sqrt(2. / n))
			elif isinstance(m, BatchNorm2d):
				m.weight.data.fill_(1)
				m.bias.data.zero_()


if __name__ == "__main__":
	model = DeepLabv3_plus(nInputChannels=3, n_classes=1, os=16, pretrained=True, _print=True)
	model.eval()
	image = torch.randn(1, 3, 256, 256)
	with torch.no_grad():
		output = model.forward(image)
	print(output.size())






